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      <a class="article-title" href="/blog/2017/07/03/remote-team/">Zapier 创始人：想要带好远程团队，你需要这样一份管理指南</a>
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        <p>随着科技的进步，很多工作已由传统上的上下班，变成了远距离的工作。这为人们提供了很多便利，但另一方面，远程工作减少了大家与同事间的社会交流，增加了冲突的机会。根据著名的Y Combinator 的保守统计，每五个远程团队中，就有一个团队的创始人会离开。Zapier 创始人Wade Foster发表了题为“how manage remote team”的文章，给出了他的建议。</p>
        
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    <article id="post-slack-tutorial" class="article article-type-post" itemscope itemprop="blogPost">
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      <a class="article-title" href="/blog/2017/06/05/slack-tutorial/">Slack完整教学与上手心得：找到正确的团队沟通之道</a>
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        <p>你听过或用过 Slack 吗？过去一年， Slack 这一个团队协作平台服务不仅在国外爆红，在国内我也看过好几个创业团队使用 Slack 来做公司内部的讯息管理，到底 Slack 有什么魅力？它是不是比起Line或 Facebook 或 Email 更能有效率的管理团队沟通呢？</p>
        
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  <time datetime="2017-06-03T13:35:56.000Z" itemprop="datePublished">2017-06-03</time>
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      <a class="article-title" href="/blog/2017/06/03/slack/">slack</a>
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        <p>本文主要从心理学层面分析了 Slack 高速发展的原因。Slack 在产品上通过 “触发器、行动、投入和奖励” 这样连环的引导，让用户迅速对产品上瘾，从而将时间、工作内容、人脉和金钱等都投入到 Slack 上。</p>
        
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    <article id="post-bigdata-learn-resource" class="article article-type-post" itemscope itemprop="blogPost">
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    <a href="/blog/2017/05/04/bigdata-learn-resource/" class="article-date">
  <time datetime="2017-05-04T14:36:45.000Z" itemprop="datePublished">2017-05-04</time>
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      <a class="article-title" href="/blog/2017/05/04/bigdata-learn-resource/">史上最全“大数据”学习资源整理</a>
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        <p>　　当前，整个互联网正在从IT时代向DT时代演进，大数据技术也正在助力企业和公众敲开DT世界大门。当今“大数据”一词的重点其实已经不仅在于数据规模的定义，它更代表着信息技术发展进入了一个新的时代，代表着爆炸性的数据信息给传统的计算技术和信息技术带来的技术挑战和困难，代表着大数据处理所需的新的技术和方法，也代表着大数据分析和应用所带来的新发明、新服务和新的发展机遇。</p>
<p>　　为了帮助大家更好深入了解大数据，云栖社区组织翻译了GitHub Awesome Big Data资源，供大家参考。本资源类型主要包括：大数据框架、论文等实用资源集合。</p>
        
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    <article id="post-20170208AI" class="article article-type-post" itemscope itemprop="blogPost">
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  <time datetime="2017-02-08T12:29:08.000Z" itemprop="datePublished">2017-02-08</time>
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      <a class="article-title" href="/blog/2017/02/08/20170208AI/">人工智能</a>
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        <h2 id="基本简介"><a href="#基本简介" class="headerlink" title="基本简介"></a>基本简介</h2><p>“人工智能”一词最初是在1956 年Dartmouth学会上提出的。从那以后，研究者们发展了众多理论和原理，人工智能的概念也随之扩展。人工智能（Artificial Intelligence），英文缩写为AI。它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器，该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。人工智能从诞生以来，理论和技术日益成熟，应用领域也不断扩大，可以设想，未来人工智能带来的科技产品，将会是人类智慧的“容器”。<br>人工智能是对人的意识、思维的信息过程的模拟。人工智能不是人的智能，但能像人那样思考、也可能超过人的智能。</p>
        
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    <article id="post-20160601Spark" class="article article-type-post" itemscope itemprop="blogPost">
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    <a href="/blog/2016/06/01/20160601Spark/" class="article-date">
  <time datetime="2016-06-01T08:28:51.000Z" itemprop="datePublished">2016-06-01</time>
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      <a class="article-title" href="/blog/2016/06/01/20160601Spark/">Apache Spark</a>
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        <h2 id="基本简介"><a href="#基本简介" class="headerlink" title="基本简介"></a>基本简介</h2><p>Apache Spark 是专为大规模数据处理而设计的快速通用的计算引擎。</p>
<p>Spark是UC Berkeley AMP lab (加州大学伯克利分校的AMP实验室)所开源的类Hadoop MapReduce的通用并行框架，Spark，拥有Hadoop MapReduce所具有的优点;但不同于MapReduce的是Job中间输出结果可以保存在内存中，从而不再需要读写HDFS，因此Spark能更好地适用于数据挖掘与机器学习等需要迭代的MapReduce的算法。</p>
<p>Spark 是一种与 Hadoop 相似的开源集群计算环境，但是两者之间还存在一些不同之处，这些有用的不同之处使 Spark 在某些工作负载方面表现得更加优越，换句话说，Spark 启用了内存分布数据集，除了能够提供交互式查询外，它还可以优化迭代工作负载。</p>
<p>Spark 是在 Scala 语言中实现的，它将 Scala 用作其应用程序框架。与 Hadoop 不同，Spark 和 Scala 能够紧密集成，其中的 Scala 可以像操作本地集合对象一样轻松地操作分布式数据集。</p>
<p>尽管创建 Spark 是为了支持分布式数据集上的迭代作业，但是实际上它是对 Hadoop 的补充，可以在 Hadoop 文件系统中并行运行。通过名为 Mesos 的第三方集群框架可以支持此行为。Spark 由加州大学伯克利分校 AMP 实验室 (Algorithms, Machines, and People Lab) 开发，可用来构建大型的、低延迟的数据分析应用程序。</p>
        
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    <article id="post-20160501Hive" class="article article-type-post" itemscope itemprop="blogPost">
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  <time datetime="2016-05-01T08:28:39.000Z" itemprop="datePublished">2016-05-01</time>
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      <a class="article-title" href="/blog/2016/05/01/20160501Hive/">Hive</a>
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        <h2 id="基本简介"><a href="#基本简介" class="headerlink" title="基本简介"></a>基本简介</h2><p>hive是基于Hadoop的一个数据仓库工具，可以将结构化的数据文件映射为一张数据库表，并提供完整的sql查询功能，可以将sql语句转换为MapReduce任务进行运行。 其优点是学习成本低，可以通过类SQL语句快速实现简单的MapReduce统计，不必开发专门的MapReduce应用，十分适合数据仓库的统计分析。另外一个是Windows注册表文件。</p>
        
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    <article id="post-20160306Hbase" class="article article-type-post" itemscope itemprop="blogPost">
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    <a href="/blog/2016/03/06/20160306Hbase/" class="article-date">
  <time datetime="2016-03-06T08:27:25.000Z" itemprop="datePublished">2016-03-06</time>
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    <a class="article-category-link" href="/blog/categories/Big-Data/">Big Data</a>
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      <a class="article-title" href="/blog/2016/03/06/20160306Hbase/">HBase</a>
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        <h2 id="基本简介"><a href="#基本简介" class="headerlink" title="基本简介"></a>基本简介</h2><p>HBase是一个分布式的、面向列的开源数据库，该技术来源于 Fay Chang 所撰写的Google论文”Bigtable:一个结构化数据的分布式存储系统”。就像Bigtable利用了Google文件系统(File System)所提供的分布式数据存储一样，HBase在Hadoop之上提供了类似于Bigtable的能力。HBase是Apache的Hadoop项目的子项目。HBase不同于一般的关系数据库，它是一个适合于非结构化数据存储的数据库。另一个不同的是HBase基于列的而不是基于行的模式。</p>
        
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  <time datetime="2016-03-01T08:28:15.000Z" itemprop="datePublished">2016-03-01</time>
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      <a class="article-title" href="/blog/2016/03/01/20160301Hadoop/">Apache Hadoop</a>
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        <h2 id="基本简介"><a href="#基本简介" class="headerlink" title="基本简介"></a>基本简介</h2><p>Hadoop是一个由Apache基金会所开发的分布式系统基础架构。</p>
<p>用户可以在不了解分布式底层细节的情况下，开发分布式程序。充分利用集群的威力进行高速运算和存储。</p>
<p>Hadoop实现了一个分布式文件系统(Hadoop Distributed File System)，简称HDFS。HDFS有高容错性的特点，并且设计用来部署在低廉的(low-cost)硬件上;而且它提供高吞吐量(high throughput)来访问应用程序的数据，适合那些有着超大数据集(large data set)的应用程序。HDFS放宽了(relax)POSIX的要求，可以以流的形式访问(streaming access)文件系统中的数据。</p>
<p>Hadoop的框架最核心的设计就是:HDFS和MapReduce。HDFS为海量的数据提供了存储，则MapReduce为海量的数据提供了计算。</p>
        
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  <time datetime="2016-02-08T08:26:11.000Z" itemprop="datePublished">2016-02-08</time>
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        <h2 id="基本简介"><a href="#基本简介" class="headerlink" title="基本简介"></a>基本简介</h2><p>大数据(big data,mega data)，或称巨量资料，指的是需要新处理模式才能具有更强的决策力、洞察力和流程优化能力的海量、高增长率和多样化的信息资产。 在维克托·迈尔-舍恩伯格及肯尼斯·库克耶编写的《大数据时代》中大数据指不用随机分析法(抽样调查)这样的捷径，而采用所有数据进行分析处理。大数据的5V特点:Volume(大量)、Velocity(高速)、Variety(多样)、Value(价值密度)、Veracity（真实性）。</p>
        
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